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peft_cvlab

https://github.com/1ranguan/peft_cvlab

Science Score: 44.0%

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    CITATION.cff file
    Found CITATION.cff file
  • ✓
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    Scientific vocabulary similarity
    Low similarity (6.1%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: 1ranGuan
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 131 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

Optimizing Engineering Vehicle Detection Performance Via Efficient Fine-Tuning (Computer Vision Homework in HUST AIA)

The code is based on mmdetection, thanks to their efforts for the Computer Vision Society.

prepare your environment

Follow mmdetection official install guide.

prepare your dataset

For using mmdetection framework, we need to convert the dataset to coco format. You can use tools/dataset_converters/yolo2coco.py to convert the data from yolo format to coco format. You can also use tools/dataset_converters/images2coco.py to create coco test files for a batch of unlabeled images.

Training from scratch

Maybe you should modify dataset path in configs/_base_/datasets/cvlab_det2.py first.

Then run bash python tools/train.py configs/cvlab/cascade-rcnn_vitb_adapt_ffn=16_expdata.py for training.

Owner

  • Name: Yiran Guan
  • Login: 1ranGuan
  • Kind: user
  • Location: Wuhan China
  • Company: HUST

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "MMDetection Contributors"
title: "OpenMMLab Detection Toolbox and Benchmark"
date-released: 2018-08-22
url: "https://github.com/open-mmlab/mmdetection"
license: Apache-2.0

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Dependencies

.github/workflows/deploy.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.circleci/docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve_cn/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
requirements/albu.txt pypi
  • albumentations >=0.3.2
requirements/build.txt pypi
  • cython *
  • numpy *
requirements/docs.txt pypi
  • docutils ==0.16.0
  • myst-parser *
  • sphinx ==4.0.2
  • sphinx-copybutton *
  • sphinx_markdown_tables *
  • sphinx_rtd_theme ==0.5.2
  • urllib3 <2.0.0
requirements/mminstall.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • mmengine >=0.7.1,<1.0.0
requirements/multimodal.txt pypi
  • fairscale *
  • nltk *
  • pycocoevalcap *
  • transformers *
requirements/optional.txt pypi
  • cityscapesscripts *
  • fairscale *
  • imagecorruptions *
  • scikit-learn *
requirements/readthedocs.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • mmengine >=0.7.1,<1.0.0
  • scipy *
  • torch *
  • torchvision *
  • urllib3 <2.0.0
requirements/runtime.txt pypi
  • matplotlib *
  • numpy *
  • pycocotools *
  • scipy *
  • shapely *
  • six *
  • terminaltables *
  • tqdm *
requirements/tests.txt pypi
  • asynctest * test
  • cityscapesscripts * test
  • codecov * test
  • flake8 * test
  • imagecorruptions * test
  • instaboostfast * test
  • interrogate * test
  • isort ==4.3.21 test
  • kwarray * test
  • memory_profiler * test
  • nltk * test
  • onnx ==1.7.0 test
  • onnxruntime >=1.8.0 test
  • parameterized * test
  • prettytable * test
  • protobuf <=3.20.1 test
  • psutil * test
  • pytest * test
  • transformers * test
  • ubelt * test
  • xdoctest >=0.10.0 test
  • yapf * test
requirements/tracking.txt pypi
  • mmpretrain *
  • motmetrics *
  • numpy <1.24.0
  • scikit-learn *
  • seaborn *
requirements.txt pypi
setup.py pypi